This document outlines some of the common mistakes that occur when using machine learning, and what can be done to avoid them. Whilst it should be accessible to anyone with a basic understanding of machine learning techniques, it was originally written for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results.

翻译：本文档概述了使用机器学习时常犯的错误以及如何避免这些错误。虽然本文档对具备机器学习技术基本理解的人群均适用，但最初是为研究型学生撰写，聚焦于学术研究中特别关注的问题，例如需要进行严谨比较并得出有效结论。内容涵盖机器学习过程的五个阶段：模型构建前的准备工作、如何可靠地构建模型、如何稳健地评估模型、如何公平地比较模型，以及如何报告结果。